The rise of Edge computing in computer vision
Enhancing computer vision with Edge computing
Edge computing and computer vision are two of the most promising technological developments of the modern era, and their convergence is taking the world by storm!
Edge computing involves moving computational processing closer to the data source, enabling lightning-fast processing and visual analysis on devices such as cameras, sensors, and mobile phones, without relying on cloud-based servers.
Due to processing data at the source, Edge computing has made real-time decision-making, increased security, reduced bandwidth requirements, and lower latency possible for computer vision applications.
In fact, Edge computing has enhanced numerous computer vision techniques, including object detection, image classification, feature extraction, and anomaly detection.
Image classification, for example, is the process that categorizes images into predefined classes or categories. These capabilities are hugely enhanced by Edge computing, which enables image classification tasks to be performed locally on devices.
Edge computing offers several benefits, including improved security, reduced latency, enhanced system reliability, and increased accessibility.
In particular, operating computer vision on Edge devices can be highly beneficial in areas with poor network connectivity, as it enables critical tasks to operate seamlessly despite network infrastructure limitations.
The convergence of Edge computing and computer vision propels computer vision into a new era of smart devices, intelligent systems, and immersive experiences.
The combination of Edge computing and computer vision offers endless possibilities for developing innovative solutions that enhance business operations and improve customer experiences.
To learn more about how Edge computing is revolutionizing computer vision, check out the articles below.
A new method for Edge-based object detection
This article discusses the difficulty of detecting 3D objects on Edge devices with limited computational power.
To address this challenge, the authors explain how they created a system called Moby that uses data from cameras and LiDAR (a remote sensing method that uses laser pulses to measure distances to objects and create 3D maps of environments) to detect objects.
When Moby was tested on a dataset called KITTI, the system reduced the time it took to detect objects by an impressive 91.9%.
2D-Empowered 3D Object Detection on the Edge
Streamlining industrial inspection
This article discusses the industrial inspection industry's hurdles in creating reliable systems that rely heavily on images.
As a solution, the authors present a new Edge system that optimizes image transfer and eliminates bottleneck issues to improve efficiency and reduce data transfer.
The article also introduces the FitOptiVis project, which works to create a reference architecture for designing and developing Edge components.
Tiny but mighty: Object detection on resource-constrained Edge devices
Although object detection at the Edge is known for its real-time capabilities, it can be challenging to implement on Edge devices, which are used in applications such as autonomous driving, outdoor robotics, and computer vision.
But (as the benefits are too good to ignore), the following article proposes a workaround.
The proposed solution is to convert neural network models from Float32 to much smaller unit8 formats using TensorFlow Lite.
This conversion reduces the model to a quarter of its original size, making it suitable for devices with limited storage. Furthermore, this approach can speed up the processing time, allowing the system to work faster.
Real-Time and Accurate Object Detection on Edge Device with TensorFlow Lite
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Unlocking efficiency and innovation in the digital age
The article below discusses how the adoption of Edge computing is increasing and highlights its benefits in reducing latency, improving performance, and increasing efficiency.
It also touches on how Edge computing is used in various industries, such as healthcare, manufacturing, and transportation.
Edge AI Market Expected to Hit $70bn by 2032
DIY Edge computing
The article below highlights the broad range of use cases for Edge computing, from automated manufacturing assembly lines to remote field offices operating drone fleets for utility and mining operations.
A common thread among these use cases is that non-IT professionals are increasingly tasked with managing technology at the network's edge.
While cloud solutions have been critical for collecting and managing data at the Edge, the cloud isn’t sufficient for every Edge processing demand.
To address this, artificial intelligence and machine learning are increasingly being used to enable self-operation and intelligent insights at the Edge, requiring IT professionals to empower non-IT professionals.
Using AI and ML to Optimize Edge IoT Performance
Unlocking the IoT with Edge AI
The following article discusses how Edge AI can process sensor data with low latency and reduced power consumption.
With Edge AI, data is processed at the source, reducing the need to send it to a centralized location for processing.
As more industries adopt IoT and other intelligent systems, Edge AI is set to play an increasingly important role in enabling these systems to operate efficiently and effectively.
Factors Affecting the Performance and Efficiency of Edge Artificial Intelligence
Xailient’s newest article
By processing and analyzing data closer to its source, businesses can optimize their data processing workflows and achieve better business outcomes, all while keeping costs low.
To find out more, check out this week’s blog, What Are the Benefits of Edge Computing?
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See you next week!